Information Retrieval Spring Web retrieval

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1 Information Retrieval Spring 2016 Web retrieval

2 The Web Large Changing fast Public - No control over editing or contents Spam and Advertisement

3 How big is the Web? Practically infinite due to the dynamic pages. The host count - more than 1 billion (1,010,251,829) computers in the Internet (Internet Domain Survey January 2014) The Indexed Web contains at least 4.62 billion pages (worldwidewebsize.com/ October, 2014). Due to the growth rate, any estimation is immediately wrong.

4 Web search engines Google Bing Yahoo! Baidu Chinese Yandex Russian DuckDuckGo same results for all users

5 Challenges Volume and distribution of data; pace of change How to find pages to index Quality and authoritativeness of documents How do you know that you can rely on what you find Expressing queries and interpreting results How to formulate queries? Most users have no education in search Interpreting queries and ranking results fast Efficient search based on poorly formulated ambiguous queries, in a very large repository

6 Variety of content Public anyone can publish Many formats: HTML. GIF, JPEG, ASCII text and PDF. Many languages on the Web Quality

7 Conversion Text is stored in hundreds of incompatible file formats e.g., raw text, RTF, HTML, XML, Microsoft Word, ODF, PDF, PowerPoint, Excel A conversion tool converts the document content into a tagged text format such as HTML or XML retains some of the important formatting information

8 Web page spam Spam Link spam: artificially increasing the link based scores of Web pages. Click spam is done by robots which specify queries and click on preselected pages or ads Term spam: artificially increasing term frequency based scores

9 Search Engine Optimization Some people often confuse Web spam with Search Engine Optimization (SEO): improve the description of the contents of a Web page improve the odds of higher ranking through better descriptions

10 Advertisement Advertising is the search engines main source of revenue. Contextual advertising Sponsored search Content match Key word bids

11 Web search Classical IR: differences Different content production anyone can procude Web pages Mass and heterogeneity of the content Different users: many non-professionals! Varying types of search goals: informational, navigational and transactional queries

12 The Web graph Directed Graph Pages: nodes Links: edges Not strongly connected In-links and out-links Average number of in-links 8-15 Not randomly distributed

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15 Crawling Finding and downloading Web pages automatically. Crawler or Spider Web, topical/focused, enterprise Challenges Volume and pace of change No control over the pages that are to be copied Deep Web, Politeness & Privacy. Two tasks: Downloading pages Finding URLs

16 Web Crawler Starts with a set of seeds, which are a set of URLs given to it as parameters Seeds are added to a URL request queue Crawler starts fetching pages from the request queue Downloaded pages are parsed to find link tags that might contain other useful URLs to fetch New URLs added to the crawler s request queue, or frontier Continue until no more new URLs or disk full

17 Crawling the Web

18 Web Crawling Web crawlers spend a lot of time waiting for responses to requests Threads enable fetching many pages at same time Can potentially flood sites with requests for pages --> politeness policies

19 Politeness policies To avoid taking up all the resources of a web server. Fetch only one page at a time from a server. Delay between requests to the same server. Request queues split into one queue per web server; most queues off limits at any one time. Very large queue required Web sites can permit or disallow crawling the site or parts of it.

20 Controlling Crawling Even crawling a site slowly will anger some web server administrators, who object to any copying of their data Robots.txt file can be used to control crawlers

21 Simple Crawler Thread

22 Focused Crawling Attempts to download only those pages that are about a particular topic used by vertical search applications Pages about a topic tend to have links to other pages on the same topic popular pages for a topic are typically used as seeds Crawler uses text classifier to decide whether a page is on topic

23 Deep Web Sites that are difficult for a crawler to find are collectively referred to as the deep (or hidden) Web much larger than conventional Web Three broad categories: private sites no incoming links, or may require log in with a valid account form results sites that can be reached only after entering some data into a form scripted pages pages that use JavaScript, Flash, or another client-side language to generate links

24 Distributed Crawling Three reasons to use multiple computers for crawling Helps to put the crawler closer to the sites it crawls Reduces the number of sites the crawler has to remember Reduces computing resources required

25 Storing the Documents Reasons to store converted document text saves crawling time when page is not updated provides efficient access to text for snippet generation, information extraction, etc. Store many documents in large files, rather than each document in a file avoids overhead in opening and closing files reduces seek time relative to read time Compound documents formats used to store multiple documents in a file e.g., TREC Web

26 Conversion and Storage The collected documents in rarely plain text. HTML, XML, PDF, Office, RTF, txt Needs to be converted to uniform text + metadata Character coding Document data store Text + structured data Needed for fast access (snippets); information extraction; saving processing cost and network load. Snippets unique to each query created dynamically

27 TREC Web Format

28 Indexes Inverted indexes Distributed due to Size Costs Efficient query processing Hierarchical A small first level index for the most common queries. A larger and slower index for the rest of the queries Dynamic: merging indexes or merging results

29 Freshness Web pages are constantly being added, deleted, and modified Web crawler must continually revisit pages to maintain the freshness of the document collection stale copies no longer reflect the real contents of the web pages

30 Freshness HTTP protocol has a special request type called HEAD that makes it easy to check for page changes returns information about page, not page itself

31 Freshness Not possible to constantly check all pages must check important pages and pages that change frequently Freshness is the proportion of pages that are fresh Optimizing for this metric can lead to bad decisions, such as not crawling popular sites Age is a better metric

32 Age Expected age of a page t days after it was last crawled: Web page updates follow the Poisson distribution on average time until the next update is governed by an exponential distribution

33 Freshness vs. Age

34 Sitemaps Sitemaps contain lists of URLs and data about those URLs, such as modification time and modification frequency Generated by web server administrators Tells crawler about pages it might not otherwise find Gives crawler a hint about when to check a page for changes

35 Sitemap Example

36 Removing Duplicates and Noise Duplicate and near-duplicate documents occur in many situations Copies, versions, plagiarism, spam, mirror sites 30% of the web pages in a large crawl are exact or near duplicates of pages in the other 70% Duplicates consume significant resources during crawling, indexing, and search Little value to most users Noise Text, links and pictures that are not related to the central content of the document Negative effect on ranking

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38 Finding Content Blocks Cumulative distribution of tags in the example web page Main text content of the page corresponds to the plateau in the middle of the distribution

39 Link extraction and analysis Links and anchor texts are extracted from the documents and stored into the document data store - with the destination pages. Used for calculating scores that are based on the link structure of the web. Anchor texts are concise topical representations of the destination document. Anchor information may be indexed even for pages not yet crawled

40 Caching Search engines need to be fast. Client side (browsers) and server side (search engine). Popular queries account for 50 % of queries. Caching answers About half of the queries are still unique Caching inverted lists of the index

41 Search and result presentation Number of results is potentially very large. Number of results shown to a user is very small. Basic Architecture Given a query 10 results shown are subset of complete result set if user requests more results, search engine can - recompute the query to generate the next 10 results - obtain them from a partial result set maintained in main memory In any case, a search engine never computes the full answer set for the whole Web

42 Ranking for Web Search Ranking based on topicality and quality Topicality: Language models, Quality/popularity/authority: Page Rank, Hubs and authorities Hubs are pages with many outlinks Authorities are pages with many inlinks

43 Challenge for ranking Identification of quality content in the Web Evidence of quality can be indicated by signals such as: - domain names - text content - links (like PageRank) Additional useful signals are provided by the layout of the Web page, its title, metadata, font sizes, etc.

44 Other challenges avoiding, preventing, managing Web spam - spammers are malicious users who try to trick search engines by artificially inflating signals used for ranking - a consequence of the economic incentives of the current advertising model adopted by search engines defining the ranking function and computing it

45 Ranking signals Signals of topicality: text content Simple word counts Full ranking algorithms such as BM25. Anchor texts Layout: titles, headings, Signals of quality Domain names Number of in-links and out-links Clicks Other: Page metadata; geographical location; language; query history; Avoiding spam spam spam

46 Link-based ranking Anchor text Number of in-links: indications of popularity and quality Shared links: indications of relations between pages Hubs and authorities

47 PageRank The basic idea is that good pages point to good pages Random walk through the Web. Random surfer wandering aimlessly between Web pages. Clicks randomly one of the links on a page, or a surprise me button. Continues browsing like this for a very long time. Eventually, the random surfer has visited every single Web page The popular pages much more often, due to following links The outlinks from popular pages influence the path much more than from less popular pages. The probability of viewing a page at any given moment is the PageRank of that page.

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50 Evaluation Monitoring ranking quality Use of standard precision-recall metrics Precision of Web results should be measured only at the top positions in the ranking, say and Based on human judgement or click-through data. click-through works well in large corpora. Clicks, dwell time,

51 Spam SPAM: repetitive, annoying behaviour? Where did the word come from? RE

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